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Researcher profile

Jiawen Chen

Jiawen Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.quant-gascond-mat.stat-mechMachine Learning

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Published work

4 published item(s)

preprint2026arXiv

Onsager vortex clusters on a sphere

We study Onsager vortex clustered states in a shell-shaped superfluid containing a large number of quantum vortices. In the incompressible limit and at low temperatures, the relevant problem can be boiled down to the statistical mechanics of neutral point vortices confined on a sphere. We analyze rotation free vortex clustered states within the mean field theory in the microcanonical ensemble. We find that the sandwich state, which involves the separating of vortices with opposite circulation and the clustering of vortices with the same circulation around the poles and the equator, is the maximum entropy vortex distribution, subject to zero angular momentum constraint. The dipole momentum vanishes for the sandwich state and the quadrupole tensor serves as an order parameter to characterize the vortex cluster structure. For given finite angular momentum, the equilibrium vortex distribution forms a dipole structure, i.e., vortices with opposite sign are separated and are accumulated around the south and north pole, respectively. The conditions for the onset of clustering, and the exponents associated with the quadrupole moment and the dipole moment as functions of energy, are obtained within the mean field theory. At large energies, we obtain asymptotically exact vortex density distributions using the stereographic projection method, giving rise to the parameter bounds for the vortex clustered states. The analytical predictions are in excellent agreement with microcanonical Monte Carlo simulations.

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preprint2026arXiv

Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks

Combinatorial complexes have unified set-based (e.g., graphs, hypergraphs) and part-whole (e.g., simplicial, cellular complexes) structures into a common topological framework. Existing topological neural networks and Weisfeiler-Lehman variants remain fragmented, lacking a unified theoretical foundation for topological deep learning. In this work, we introduce the Combinatorial Complex Weisfeiler-Lehman (CCWL) test, an axiomatic-style extension of the WL test to combinatorial complexes. CCWL formalizes topological message passing through four types of neighborhood relation and provides a unified perspective on the expressive power of higher-order variants. We further prove that upper and lower neighborhoods are sufficient among the four adjacent WL tests to reach the expressivity of the full CCWL framework across topological structures of combinatorial complexes. Building on this framework, we also propose the Combinatorial Complex Isomorphism Network (CCIN) and evaluate it on synthetic and real-world benchmarks. Experimental results indicate CCIN outperforms baseline methods and offers a generalized expressive framework for topological deep learning.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement

Modern smartphones can continuously stream multi-megapixel RGB images at 60Hz, synchronized with high-quality 3D pose information and low-resolution LiDAR-driven depth estimates. During a snapshot photograph, the natural unsteadiness of the photographer's hands offers millimeter-scale variation in camera pose, which we can capture along with RGB and depth in a circular buffer. In this work we explore how, from a bundle of these measurements acquired during viewfinding, we can combine dense micro-baseline parallax cues with kilopixel LiDAR depth to distill a high-fidelity depth map. We take a test-time optimization approach and train a coordinate MLP to output photometrically and geometrically consistent depth estimates at the continuous coordinates along the path traced by the photographer's natural hand shake. With no additional hardware, artificial hand motion, or user interaction beyond the press of a button, our proposed method brings high-resolution depth estimates to point-and-shoot "tabletop" photography -- textured objects at close range.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer

Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent approaches, based on deep neural networks, produce impressive results but are either too slow to run at practical resolutions, or still contain objectionable artifacts. We propose a new end-to-end model for photorealistic style transfer that is both fast and inherently generates photorealistic results. The core of our approach is a feed-forward neural network that learns local edge-aware affine transforms that automatically obey the photorealism constraint. When trained on a diverse set of images and a variety of styles, our model can robustly apply style transfer to an arbitrary pair of input images. Compared to the state of the art, our method produces visually superior results and is three orders of magnitude faster, enabling real-time performance at 4K on a mobile phone. We validate our method with ablation and user studies.

0 citations0 reviewsNo code linkedOpen access